Non-connected vehicle detection using connected vehicles - Phase 1 and 2

Connected vehicle (CV) technologies are entering the realm of deployment. They have the potential to help drivers make safe, reliable and informed decisions, and thereby to enhance network capacity and reduce congestion. During the transition to CV technologies, there will be mixed traffic streams of CVs and non- CVs. While most studies have analyzed the CV car-following behavior in a pure CV environment, there is the need for a comprehensive CV car-following model in general mixed flow environment. Further, to improve the efficiency and reliability of traffic operations under mixed flow environments, there is the need to observe not only CV trajectory data, but also non-CV location/trajectory. This study proposes a mixed flow CV car- following model which will generate realistic CV trajectories in a mixed flow environment. Then, a Hidden Markov Model, which is a probabilistic inference approach, is proposed to identify non-CV locations/trajectories. This methodology will be integrated with a cooperative situational awareness framework.The proposed model will be analyzed using real-world vehicle trajectory data to aid the situational awareness of CVs under low market penetration rates.

• Record URL:
  ccat.umtri.umich.edu

Language

• English

Project

• Status: Completed
• Funding: $Total $258,364: $119,859 Purdue CS - $138,505 CCAT
• Contract Numbers:

  69A3551747105

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Connected and Automated Transportation

  University of Michigan Transportation Research Institute
  Ann Arbor, MI  United States  48109
• Project Managers:

  Tucker-Thomas, Dawn

• Performing Organizations:

  Purdue University, Lyles School of Civil Engineering

  550 Stadium Mall Drive
  West Lafayette, IN  United States  47907
• Principal Investigators:

  Peeta, Srinivas

• Start Date: 20170801
• Expected Completion Date: 20220930
• Actual Completion Date: 20240102
• USDOT Program: University Transportation Centers Program
• Subprogram: Research

Subject/Index Terms

• TRT Terms: Alertness; Detection and identification; Intelligent vehicles; Markov chains; Mobile communication systems; Vehicle detectors; Vehicle to vehicle communications
• Subject Areas: Data and Information Technology; Highways; Research; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01666039
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3551747105
• Files: UTC, RIP
• Created Date: Apr 13 2018 2:25PM